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Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking

Authors:

Katarzyna Budzynska,

Chris ReedAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 17, Issue 3

Article No.: 25, Pages 1 - 22

https://doi.org/10.1145/3032989

Published: 09 July 2017 Publication History

Abstract

Governments around the world are increasingly utilising online platforms and social media to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. In this article, we show how the analysis of argumentative and dialogical structures allows for the principled identification of those issues that are central, controversial, or popular in an online corpus of debates. Although areas such as controversy mining work towards identifying issues that are a source of disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to the discussion, attracting proportionately more support than others, and those that are divisive, attracting proportionately more conflicting viewpoints.

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Cited By

Carstens JFriess D(2024)AI Within Online Discussions: Rational, Civil, Privileged?Minds and Machines10.1007/s11023-024-09658-034:2Online publication date: 4-May-2024
https://doi.org/10.1007/s11023-024-09658-0
Romberg JEscher T(2023)Making Sense of Citizens’ Input through Artificial Intelligence: A Review of Methods for Computational Text Analysis to Support the Evaluation of Contributions in Public ParticipationDigital Government: Research and Practice10.1145/36032545:1(1-30)Online publication date: 3-Jun-2023
https://dl.acm.org/doi/10.1145/3603254
Bistarelli STaticchi C(2023)On the study of acceptability in weighted argumentation frameworks through four-state labelling semanticsJournal of Logic and Computation10.1093/logcom/exad03933:8(1872-1897)Online publication date: 8-Jun-2023
https://doi.org/10.1093/logcom/exad039
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Index Terms

Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking
1. Applied computing
  1. Computers in other domains
    1. Computing in government
      1. E-government
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics
  2. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 17, Issue 3

Special Issue on Argumentation in Social Media and Regular Papers

August 2017

201 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3106680

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

Issue’s Table of Contents

Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2017

Accepted: 01 December 2016

Revised: 01 November 2016

Received: 01 January 2016

Published in TOIT Volume 17, Issue 3

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Cited By

Carstens JFriess D(2024)AI Within Online Discussions: Rational, Civil, Privileged?Minds and Machines10.1007/s11023-024-09658-034:2Online publication date: 4-May-2024
https://doi.org/10.1007/s11023-024-09658-0
Romberg JEscher T(2023)Making Sense of Citizens’ Input through Artificial Intelligence: A Review of Methods for Computational Text Analysis to Support the Evaluation of Contributions in Public ParticipationDigital Government: Research and Practice10.1145/36032545:1(1-30)Online publication date: 3-Jun-2023
https://dl.acm.org/doi/10.1145/3603254
Bistarelli STaticchi C(2023)On the study of acceptability in weighted argumentation frameworks through four-state labelling semanticsJournal of Logic and Computation10.1093/logcom/exad03933:8(1872-1897)Online publication date: 8-Jun-2023
https://doi.org/10.1093/logcom/exad039
Lawrence JVisser JReed C(2023)Translational argument technologyWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2023.10078677:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.websem.2023.100786
Lawrence JPereira-Fariña MVisser J(2023)Argument Mining and Analytics in ArchaeologyDiscourse and Argumentation in Archaeology: Conceptual and Computational Approaches10.1007/978-3-031-37156-1_12(263-290)Online publication date: 28-Jun-2023
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Bistarelli STaticchi C(2022)A Labelling Semantics and Strong Admissibility for Weighted Argumentation FrameworksJournal of Logic and Computation10.1093/logcom/exab085Online publication date: 19-Jan-2022
https://doi.org/10.1093/logcom/exab085
Kopf SKopf S(2022)Wikipedia Talk Page Discourse IA Discursive Perspective on Wikipedia10.1007/978-3-031-11024-5_3(59-90)Online publication date: 6-Oct-2022
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Martin-Rodilla PSánchez M(2020)Software Support for Discourse-Based Textual Information Analysis: A Systematic Literature Review and Software Guidelines in PracticeInformation10.3390/info1105025611:5(256)Online publication date: 7-May-2020
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